The Northeast United States has experienced the most rapidly increasing occurrences of extreme precipitation within the U.S. over recent decades, particularly during the warm season. This trend is primarily linked to events associated with tropical cyclones. Understanding the drivers leading to long-term trends in regional extreme precipitation under different future climate scenarios is critical to adaptation and mitigation planning.
New simulations with the fully-coupled 25-km GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless System for Prediction and EArth System Research) model and its 10 ensemble members, present a unique opportunity to study changes in regional extreme precipitation and relevant physical processes. Under the SSP5-8.5 scenario, SPEAR projects top 1% extreme precipitation events over the Northeast U.S. to increase by up to 2.4% by the end of the 21st century. The projected increase is driven by higher anthropogenic radiative forcing and is distinguishable from natural variability by the mid-century. From the meteorological perspective, the occurrences of warm season extreme precipitation related to both atmospheric rivers and tropical cyclones are projected to increase, even though the frequency of tropical cyclones in the North Atlantic is projected to decrease in the model.
The SSP5-8.5 scenario, however, represents a highly unlikely trajectory, prompting the scientific community to explore scenarios with rapid reductions in greenhouse gas (GHG) concentrations through various climate mitigation efforts. Using the SSP5-3.4OS overshoot scenario from the SPEAR model—where GHG emissions decline sharply after 2040 and reach net-negative levels by 2070—we assess the impact of mitigation on extreme precipitation over the Northeast U.S. Our results show that extreme precipitation frequency over the Northeast U.S. is projected to decrease as GHG concentrations decline. However, the timing of this reversal is seasonally dependent: warm-season trends reverse shortly after global mean surface temperature starts to decline, while cold-season trends lag by approximately 15 years. These results suggest that the response of extreme precipitation to GHG reductions may depend on the underlying mechanisms driving these events. For example, cold-season extremes are more often associated with large-scale extratropical cyclones, where dynamical processes play a significant role. Our study underscores the urgent need for a deeper understanding of the physical processes governing regional climate extremes in response to GHG mitigation. Such insights are essential for informing adaptation strategies and policymaking for effective climate risk management.
How to cite: Jong, B.-T., Delworth, T., Labe, Z., Cooke, W., and Murakami, H.: Attributing changes in extreme precipitation across the Northeast U.S. under different climate scenarios , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10906, https://doi.org/10.5194/egusphere-egu25-10906, 2025.
While the influence of well-mixed greenhouse gas emissions on global warming is well-documented and robustly attributed through multiple lines of evidence, regional attribution remains more challenging and dependent on the performance and resolution of climate models. This study proposed an observational-based statistical analysis utilizing the real-time Global Warming Index (GWI) to investigates the extent to which observed regional temperature trends are attributable to global-scale anthropogenic factors, mainly the direct effects of CO2, aiming to differentiate the portion of the change attributable to regional-scale drivers (such as regional industrial aerosols, black carbon aerosols, and land-use/land-cover change, etc).
To quantify the contribution of global– and regional–scale climate drivers to observed temperature change, I performed regression analyses using the HadCRUT4 temperature data and the Global Warming Index (GWI). The GWI, calculated through a least squares method, correlates observed global average temperatures with expected responses to global radiative forcing series.
Results indicate that in certain regions — specifically West Asia, East N. America, West Africa, and the Amazon basin — 62±7%, 61±13%, 58±7%, and 61±10% of the warming observed over 1991-2020 can be attributed to global anthropogenic warming, primarily the direct effects of CO2. The remaining portion, which represents 22±8%, 21±16%, 27±9%, 23±14% of the observed warming, is attributable to regional climate drivers. The Mediterranean showcases high sensitivity, with regional drivers contributing 31±13% of the observed 1.2°C warming, amplifying the warming attributed to global anthropogenic drivers. The Arctic Ocean along with the Russian-Arctic region exhibits a substantial contribution from regional drivers and local feedback mechanisms to the observed warming amplification, quantified at 43±15% of the 3°C warming over 1991-2020. Regional cooling drivers, however, are significant in East Asia and the Tibetan Plateau, with the latter experiencing a cooling contribution of -42±17% (with a ±95% uncertainty due to internal variability derived from control simulations).
The novel approach presented in this study helps in understanding how different scales of climate change drivers contribute to local temperature change. This understanding can foster more effective, localized mitigation strategies that complement global efforts to address climate change.
How to cite: Barkhordarian, A.: Disentangling Regional Climate Change: Assessing the contribution of global– and regional–scale anthropogenic drivers to observed regional warming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11006, https://doi.org/10.5194/egusphere-egu25-11006, 2025.
The record-breaking global mean surface temperature (GMST) in 2023 came as a surprise to the scientific community, raising the question whether 2023 provides evidence for a recent and abrupt increase in the global warming rate. Here, we quantify the variability and forced contribution to annual GMST by training a statistical learning model on surface temperature anomalies from historical and future climate model simulations. Our method presents a novel approach to separting variability from forced changes in GMST, providing a computationally simple and powerful alternative to existing atmospheric Green’s function approaches. We find that more than half of the 2023 jump in GMST is explained by internal variability, largely owing to anomalously cool conditions in 2022. An unlikely combination of strong but not unprecedented forced and internal contributions occurring simultaneously appears to have led to the extreme jump in 2023 GMST, with North Atlantic warming being a key contributor. When adjusting for variability, we find a steady increase in forced warming rate over the past decades, consistent with previous studies. There is insufficient evidence for an acceleration of forced warming in 2023 and 2024 beyond the expected increase from continued carbon dioxide emissions and decreasing aerosol forcing in the past decades. Our results highlight the role of internal variability for short-term GMST fluctuations and call for an improved understanding of the Atlantic warming observed in 2023.
How to cite: Gyuleva, G., Knutti, R., and Sippel, S.: Combination of Internal Variability and Forced Response Reconciles Observed 2023 Warming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10729, https://doi.org/10.5194/egusphere-egu25-10729, 2025.
Global mean annual temperature in 2023 did end up much warmer than anticipated, stirring up a lively debate as to what the potential reasons might be. 2024 continued that trend with another record warm year on top, exceeding 1.5°C globally for the first time in all temperature data sets on an annual basis.
Here we argue (1) that 2023 is entirely compatible with our understanding of the climate system and (2) that the so-called ‘hiatus‘ controversy from the early 2010s should be used as a reminder to be rather cautious with claims that suggest something puzzling might be going on.
We present results from statistical and model based analysis, demonstrating that the magnitude of the new September and annual temperature record in 2023 lies within the range of possible record margins under current warming / forcing conditions. We also show that random shifts in large scale circulation patterns led to record warm conditions in the North Atlantic and Antarctica, contributing to the 2023 and 2024 outcome (in addition to anthropogenic factors as well as El Niño).
We also discuss whether or not these shifts are partially attributable (directly or indirectly) to the 2022 Hunga Tonga eruption or the regulation-induced sulphur emissions reduction in the global shipping sector.
How to cite: Haustein, K., Theisen, N., and Sippel, S.: Lessons from the ‘hiatus‘ controversy for the 2023/2024 warming spike, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7065, https://doi.org/10.5194/egusphere-egu25-7065, 2025.
Historical changes in the North Atlantic atmospheric and oceanic circulation are re-evaluated using output from the Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP). We focus on five of the single forcing experiments included in Phase 1 of the LESFMIP protocol: hist-GHG, hist-aer, hist-volc, hist-solar, and hist-totalO3. For each of these five, at least 10 ensemble members have been simulated over the period 1850 to 2020 by ~10 models. This dataset offers an unprecedented view of how these forcings have affected surface climate and the tropospheric and oceanic circulation, and their associated extremes. Specifically, the large-ensemble allows for isolating weak signals that otherwise would be buried under internal variability, while also offering a testbed for methods to extract predictable signals with correct amplitude.
Preliminary work shows a clear effect of greenhouse gases and aerosols on jets. In June-August, the influence of aerosols is as strong as that of greenhouse gases. Furthermore, the inter-model spread in the NH vortex responses dominates the intermodel spread in the NAO response. Ongoing work is aimed at formulating emergent constraints to sort out intermodel differences in the forced response of the polar vortex to historical forcings. Ongoing work is also aimed at understanding the impacts on surface temperature and precipitation. This is a community effort from the WCRP's APARC LEADER and EPESC projects.
How to cite: Avisar, D., Kuchař, A., Garfinkel, C., and Simpson, I.: Understanding historical changes in the North Atlantic atmospheric and oceanic circulation: insights from the Large Ensemble Single Forcing Model Intercomparison Project , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9609, https://doi.org/10.5194/egusphere-egu25-9609, 2025.
Coastal sea level extremes are among the potentially most hazardous events for the densely populated coastal regions. Changes in extreme sea levels, combined with rising mean sea level, increase coastal vulnerability, and will continue to do so in the future. It is thus necessary to understand and quantify the role of global warming in the likelihood and intensity of extreme sea levels. In this study, we aim at testing whether the probability of extreme sea levels has changed in any way due to global warming. To do so, we analyse annual sea level maxima of a large ensemble of hydrodynamic simulations along the European coasts forced with the outputs of state-of-the-art climate models, simulating a total of nearly 1800 years of data that are representative of the climate of the past 6 decades, as well as 2500 years of data that are representative of the pre-industrial climate. The data have been bias-corrected to improve their reliability and accuracy in representing local sea level variations. We rely on the largely extended dataset to compute the Fraction of Attributable Risk (FAR) for different sets of sea level extremes along the entire European coastline. The results reveal that present-day regional climate conditions are altering the probability of likelihood of extreme sea levels along a large fraction of the European coastlines.
How to cite: Mulet, M., Marcos, M., Amores, A., and Agulles, M.: Influence of Global Warming on Extreme Sea Level Events Along European Coasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8966, https://doi.org/10.5194/egusphere-egu25-8966, 2025.
Summer heat waves pose health threats to the general population, in particular, vulnerable groups. The skill for seasonal prediction of such heat waves has recently advanced. Yet, whether forecast information on this time scale, a time scale at which individual preparedness could still be improved, might be taken up by the general population, has—so far—not been investigated. Here, we present results from a large-scale online experiment with a general population sample in Germany (N = 4,251) to test how households respond to risk assessments for the number of heat events in their regions for the summer of 2024. Heat events are the number of tropical nights, i.e., with a temperature minimum of at least 20°C, during summer 2024 (June 1st – August 31st). As a risk assessment we use the 75percentile of an ensemble with 30 members originating from the operational seasonal forecasts with the German Climate Forecast System (GCFS2.1, Deutscher Wetterdienst, DWD). Participants were exposed in May 2024 to forecast information for the number of heat events in their region of residence, and in addition were provided with the typical summer in the absence of anthropogenic climate change. We present the consequential choices for self-interested and altruistic preventive adaptation measures, as well as for support for mitigation efforts participants demonstrated. Our results identify the impact of ’forward attribution’ to climate change, i.e., information on how the risk assessments would have differed for a world without anthropogenic climate change. We also check whether the perceived reliability of the seasonal prediction spills over to the perceived reliability of long-term climate predictions.
How to cite: Baehr, J., Borchert, L., Brune, S., Damania, M., Drupp, M., Lange, A., Longo, E., Asthana, S., and Perino, G.: Anticipating Hot Summer Nights, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15359, https://doi.org/10.5194/egusphere-egu25-15359, 2025.
Global warming as well as human modification of the Earth’s surface profoundly affects the water cycle in regional climates. A key question for ecosystem health and humanity in general is how exactly water resources and water-induced hazards will be affected. Atmospheric moisture tracking methods have the potential to help unravel the mechanisms of changes in precipitation patterns.
In the climate we had around the year 2000, moisture tracking tools have shown that about 40% of the rainfall on land originated from the land itself and 60% was supplied by the oceans. Several studies have also indicated that due to the land being water-limited for evaporation, the relative importance of the oceans will increase in a warming climate. For more detailed moisture tracking studies into past and future climates, however, the provided data from climate model intercomparison projects is often a limiting factor.
In this presentation, we present a position paper that aims to unlock the potential of addressing novel research questions by combining climate modelling and moisture tracking. First, we review the state-of-the-art regarding moisture tracking with climate models. Second, we present the data requirements for moisture tracking models, which typically consist of a limited set of surface and atmospheric variables, but have specific requirements regarding temporal, horizontal and vertical resolution. Third, we evaluate typical uncertainties in moisture tracking that may arise from working with suboptimal resolutions. Fourth, we analyze to what extent some climate models are already providing sufficient data to perform moisture tracking studies. data request. Fifth, we map potentially interesting research avenues linked to specific Model Intercomparison Projects (MIPs) within the ongoing CMIP6 to illustrate how more synergies could be created.
In conclusion, we systematically evaluated the current research interest, limitations and potential for moisture tracking studies with climate model output. With this presentation we hope to stimulate CMIP7 and other climate data providers to work together with the moisture tracking community to align the supply and demand side of climate variables. Doing so, would allow us to tap the now largely untapped potential of using moisture tracking to gain more insight into past and future water cycle changes.
How to cite: van der Ent, R., Benedict, I., Deman, V., Insua-Costa, D., Kalverla, P., Koning, H., Koren, G., Lokkart, C., Schilperoort, B., Staal, A., Wang-Erlandsson, L., Weijenborg, C., and Yang, K.: Research opportunities for combining climate models with moisture tracking, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6337, https://doi.org/10.5194/egusphere-egu25-6337, 2025.
In 2022, Europe experienced a severe and extensive drought with substantial ecological and economic impacts. The climatic hazard that led to these impacts can be attributed to two primary causes. First, thermodynamic warming due to climate change reduces water availability through increased evaporative demand. Second, an unusual atmospheric circulation pattern during the event compounded the situation. This was further exacerbated by strong decadal trends in atmospheric circulation. While thermodynamic changes are physically well understood, our understanding of the impact of circulation-driven trends on climate is largely limited to its impact on trends in surface temperature. To attribute the role of these different climatic drivers on the drought impacts, we use a storyline approach by nudging the Community Earth System Model Version 2 (CESM2) to atmospheric circulation patterns from the ERA5 reanalysis data at different forcing levels. Our findings indicate that the dynamical conditions leading to the 2022 drought were the most extreme in the observed period, following a long-term atmospheric circulation trend that explains around 50% of European drying. Moreover, the 2022 circulation patterns not only intensified the drought but also interacted with thermodynamic effects, exacerbating the hydroclimatic impacts. By distinguishing between circulation-induced trends and thermodynamic changes, we provide a nuanced understanding of the drivers of European hydroclimatic variability and their contribution to extreme events. We highlight the critical need to consider both atmospheric circulation changes and thermodynamic influences to evaluate accurately and project future hydroclimatic trends in Europe.
How to cite: Dunkl, I., Sippel, S., and Bastos, A.: Disentangling the role of trends in Atmospheric Circulation Patterns from Thermodynamic Effects for European Hydroclimate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18544, https://doi.org/10.5194/egusphere-egu25-18544, 2025.
Anthropogenic climate change is unfolding rapidly, yet its regional manifestation is often obscured by atmosphere-ocean internal variability. A primary goal of climate science is to identify the forced response, i.e., spatiotemporal changes in climate in response to greenhouse gases, anthropogenic aerosols, and other external forcing, amongst the noise of internal climate variability. Separating the forced response from internal variability can be addressed in climate models by using a large ensemble to average over different possible realizations of internal variability. However, with only one realization of the real world, it is a major challenge to isolate the forced response in observations, as is needed for attribution of historical climate changes, for characterizing and understanding observed internal variability, and for climate model evaluation.
In the Forced Component Estimation Intercomparison Project (ForceSMIP), contributors used existing and newly developed statistical and machine learning methods to estimate the forced response during the historical period within individual ensemble members and observations, across eight key climate variables (SST, surface air temperature, precipitation, SLP, zonal-mean atmospheric temperature, monthly max. and min. temperature, and monthly max. precipitation). Participants could use five CMIP6 large ensembles to train their methods, but they then had to apply their methods to individual evaluation members, the identity of which was hidden. Participants used methods including regression methods, convolutional neural networks, linear inverse models, fingerprinting methods, and low-frequency component analysis. Here we show how the different methods performed on climate models and what they determined to the be the forced response in observations. Our results show that many different types of methods are skillful for estimating the forced response and that the most skillful method depends highly on which variable and metric is evaluated. Furthermore, methods that show comparable skill can give very different estimates of the forced response in observations, illustrating the epistemic uncertainty in estimating the forced climate response from observations. ForceSMIP gives new insights into the forced response in observations across multiple key variables, but also the remaining uncertainty in its estimation.
How to cite: Jnglin Wills, R., Deser, C., McKinnon, K., Phillips, A., Po-Chedley, S., Sippel, S., and Merrifield, A. and the ForceSMIP Tier1 Contributors: Forced Component Estimation Statistical Methods Intercomparison Project (ForceSMIP), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17706, https://doi.org/10.5194/egusphere-egu25-17706, 2025.
The attribution of global and regional climate change to anthropogenic greenhouse gases (GHGs) is well appreciated. Existing estimates based on radiative forcing studies suggest CO2 dominates global warming since 1850 with CH4 the second largest contribution (33-66% of CO2). However, radiative forcing studies involve several assumptions and GHG attribution beyond global-mean warming is unknown. Here we quantify the impact of individual GHGs on global warming indicators and regional climate change using single-forcing historical experiments of CO2, CH4, and other GHGs. CO2 is shown to dominate global warming in the satellite era with CH4 only 20% of the CO2 contribution, smaller than the amplitude of internal variability. Methane is also a small contribution for other global warming indicators, including Arctic Sea ice loss, extreme temperatures and continental scale warming. The results demonstrate that, on multi-decadal or longer time scales, CO2 is the dominant control knob and the contribution of CH4 to regional climate change is very small, undistinguishable from noise. Thus, CH4 mitigation may not be as effective as previously thought, particularly for regional scale impacts.
How to cite: Shaw, T., Toda, M., and Kang, S.: Minimal impact of methane on satellite-era climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12604, https://doi.org/10.5194/egusphere-egu25-12604, 2025.
Projections from a CMIP6 multimodel ensemble show weak signal of climate change in annual and seasonal precipitation over most of South America (SA), with low agreement among models as to the sign of the change over most of the continent. Besides, climate change information from different analyses frequently seems confusing for the public and decision makers. Since climate has a crucial influence on important economic sectors in SA, such as hydroelectric power generation and agriculture, and natural disasters associated with extreme events of drought and excessive rainfall have become more frequent and intense, the future climate behavior should be more clearly described, and supported by a dynamical framework able to explain it, so as to better serve decision-makers in planning actions and adopt effective policies for climate adaptation.
Although weak and with low agreement between models, the climate change projected by the CMIP6 multimodel ensemble for SA shows similarity with the seasonal impacts of El Niño (EN) events on precipitation. Since model projections of future SST indicate an El Niño-like warming pattern in the central-east equatorial Pacific, it is reasonable to hypothesize that changes in precipitation over South America would have the patterns of EN impact and would be mainly due to the strengthening of an EN-type SST anomaly pattern in the Pacific Ocean.
Therefore, to clearly determine the future climate changes, it is necessary to select models that not only simulate well the SA climatology, but also the El Niño-Southern Oscillation (ENSO) and its teleconnections with SA, since ENSO is responsible for most of the climate variability in SA. The assessment covered 31 models that provided at least three runs from the present (1979-2014) to the future climate (2065-2100). Based on relevant and comprehensive criteria, the models were classified according to both assessments (climatology and ENSO), and four best-performing models were selected.
The changes projected by the ensemble of best models indicate a more EN-like future climate, in which the main climate changes projected for SA resemble the observed EN impacts, remarkably including the tendency to spring-summer reversal of precipitation anomalies in Central-East SA, from dryer spring to wetter summer. While the total monsoon precipitation shows little or no change in this region, there is reduction (enhancing) of early (peak) monsoon rainfall, resulting in a delay and shortening of the monsoon season. The spring response in this region is due to the dynamical effect of the EN-like SST changes via teleconnection, and the reversal in summer is triggered by surface-atmosphere interactions. Also coherently with EN impacts, drier conditions prevail in central-northern-eastern Amazon throughout the monsoon season thanks to changes in the Walker circulation, while in southeast SA, precipitation increases due to tropics-extratropics teleconnection.
The changes projected by the all-model ensemble are much weaker and confusing. This clear description of climate change and its dynamical connection with intensified EN effects give coherence to the different changes throughout different seasons, which otherwise seem incomprehensible and can lead to discrepant interpretations if not understood within a correct dynamic context.
How to cite: Grimm, A. M. and Padoan, D.: Explaining climate change in South America, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-979, https://doi.org/10.5194/egusphere-egu25-979, 2025.
How to cite: Hohenegger, C., Warnau, S., Shaw, T., and Kang, S.: New look at humidity trends, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6554, https://doi.org/10.5194/egusphere-egu25-6554, 2025.
Uneven economic impacts of climate change have been caused by differentiated warming rates across different geographical regions, threatening the well-being of millions to billions of people. Region-dependent historical and future warming rates are often obtained from global climate models, which, however, exhibit wide spreads in both global mean temperature change and regional deviates. While the multi-model spread in global mean warming rate has been widely reported in past literature, the multi-model spread in terms of global warming pattern and its temporal evolution remain unclear. Here we show that the multi-model spread in the simulated global warming pattern is dependent on the level of warming. We find that the simulated global warming pattern deviates substantially among CMIP6 models before 1985. The multi-model consistency rises afterwards, as the greenhouse gases level and global mean warming rate increase. Furthermore, the consistency of model-predicted future warming pattern varies by emission scenario. Models predict highly consistent warming patterns under the high emission scenario during the entire 21st century; whereas under low and intermediate emission scenarios, future warming patterns diverge among these models around middle of the 21st century. While our study detects the anthropogenic signal in the temporal evolution of multi-model consistency in the global warming pattern, the physical mechanisms underlying such varying multi-model consistency in the warming pattern merits further investigation.
How to cite: Meng, Y., Yu, Y., and Nie, J.: Reduced spread of simulated global warming patterns among CMIP6 models with accelerated pace of warming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8252, https://doi.org/10.5194/egusphere-egu25-8252, 2025.
Climate trends represent one source of predictability for climate forecasts. Hence, it is important for seasonal prediction systems to reproduce observed trends in the climate system. This contribution presents an assessment of trends in seasonal hindcasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). We investigate trends in the tropics and extratropics, as well as potential links between those.
In the tropics, the focus is on processes related to El Nino – Southern Oscillation (ENSO). The hindcasts exhibit a spurious sea surface warming trend in the equatorial Pacific (i.e., a tendency towards El Nino in more recent years), which is mainly related to an underestimation of the atmospheric circulation response to the observed strengthening of the equatorial Pacific zonal sea surface temperature gradient. The trend errors are most pronounced for boreal summer and autumn (independent of start date). Furthermore, the trend errors are similar to those found in free coupled climate model simulations and suggest a biased response of the atmospheric model to changes in the anthropogenic forcing.
In the extratropics, we focus on summer-time upper tropospheric circulation and surface temperature. The ensemble mean completely misses the clear observed circulation trends (resembling a wave-5 pattern) and the associated centers of enhanced surface warming. Possible implications and causes of the missed trends will be discussed.
How to cite: Mayer, M., Befort, D., and Weisheimer, A.: Representation of tropical and extratropical trends in ECMWF seasonal hindcasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17291, https://doi.org/10.5194/egusphere-egu25-17291, 2025.
Understanding past climate conditions is essential for addressing future climate challenges. However, observational climate datasets often contain missing values, especially in older records, leading to incomplete and inaccurate analyses. Interpolation methods like kriging are commonly employed to address this issue by filling data gaps. Nevertheless, these approaches often fail to effectively reconstruct complex climatic patterns [1, 2].
This study leverages the transformative power of deep learning to accurately reconstruct two observational datasets. The first dataset is an intermediate product of HadEX3 [3], which contains gridded extreme indices over land regions, such as the TX90p index, corresponding to the percentage of days where daily maximum temperature is above the 90th percentile. The second dataset is the Full data GPCC product [4], containing global precipitation fields at monthly frequency. To reconstruct these two datasets with high accuracy, we employ and compare three deep learning approaches: a U-Net with partial convolutional layers, a diffusion model and a graph neural network. In all cases, models are trained on CMIP6 climate model data, evaluated on unseen CMIP6 and ERA5 data and compared to Kriging. The best-performing models are then applied to the observational datasets, providing new insights into historical climate conditions to inform more effective climate adaptation strategies. The reconstructed datasets are being prepared for the community in the framework of the H2020 CLINT project [5] and the Horizon Europe EXPECT project [6].
[1] Kadow C. et al., Nat. Geosci., 13, 408-413 (2020)
[2] Plésiat É. et al., Nat. Commun., 15, 9191 (2024)
[3] Dunn R.J.H. et al., J. Geophys. Res. Atmos., 125, 1 (2020)
[4] Schneider, U. et al., DOI: 10.5676/DWD_GPCC/FD_M_V2022_100 (2022)
[5] https://climateintelligence.eu/
[6] https://expect-project.eu/
How to cite: Plésiat, É., Dunn, R. J. H., Donat, M., and Kadow, C.: Reconstructing Historical Climate Data using Deep Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18816, https://doi.org/10.5194/egusphere-egu25-18816, 2025.
Understanding the dynamics of land surface temperature (LST) and sea surface temperature (SST) in coastal regions is crucial for addressing climate change impacts. This study investigates 20 years of MODIS-derived LST and SST data to assess the influence of land cover transformations on temperature patterns in the Indian Peninsula. Contrary to the prevailing emphasis on urbanization, our analysis reveals that shifts from forested areas to agricultural or barren lands have a more significant impact on temperature dynamics. Using geospatial techniques, we identify long-term trends and quantify the relative contributions of various land cover types to LST and SST variations. The findings highlight the critical role of non-urban land use changes in coastal temperature dynamics, challenging traditional perspectives. This study provides actionable insights for sustainable land management and climate adaptation strategies in coastal regions, emphasizing the need for integrated land use planning to mitigate thermal vulnerabilities in the face of global climate change.
How to cite: Halder, D., Pritipadmaja, P., and Garg, R. D.: Evaluating the influence of Land Cover transformation on Coastal Land and Sea Surface Temperature Dynamics in Indian Peninsula, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-796, https://doi.org/10.5194/egusphere-egu25-796, 2025.
Land surface and the planetary boundary layer are linked by the water and energy cycles, and the effects of soil water-air coupling modulate near-surface temperatures. In particular, late spring soil moisture anomalies may uncover predictability to the system, and then contribute to predictions of extreme events such as heatwaves at the subseasonal to seasonal scale. In this study, we use a data-driven seasonal forecast for summer heat waves in the Western Mediterranean, and a downstream explainable artificial intelligence helps us separate the individual contribution of the predictors and quantify and rank them in terms of their relative importance. Soil moisture emerges as one of the heat wave predictors, along with SST in the North Atlantic and the background global warming. Results show that soil largely contributes to heatwaves predictability when it is dry at the beginning of the season, otherwise its importance is limited. In addition, soil moisture contribution substantially increases from the beginning of the 1990s, when the local warming quickly arises and summer precipitation declines sharply. When atmospheric patterns are favorable for the advection of hot and dry air, conditions for persisting and more intense heatwaves are supported by an interacting land surface. With little water available for evaporation, the increased atmospheric evaporative demand may not be met, therefore the lack of latent cooling in the atmosphere enables more intense and persistent heatwaves.
How to cite: Materia, S. and Donat, M.: Growing importance of soil moisture anomalies for prediction of summer heatwaves in the Western Mediterranean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20837, https://doi.org/10.5194/egusphere-egu25-20837, 2025.
Posters on site: Wed, 30 Apr, 10:45–12:30 | Hall X5
The recent trend in the Northern Hemisphere summer atmospheric circulation resembles a Rossby wave with wave number 5. These quasi-stationary circumglobal Rossby waves are associated with extreme events, such as heatwaves, droughts, and floods, that can have catastrophic societal impacts. Therefore, understanding the drivers of these Rossby waves and evaluating their representation in climate models is a key scientific challenge. However, identifying the drivers of such patterns can be difficult because traditional approaches such as simple correlation analysis may not capture the complex, nonlinear interactions inherent in atmospheric teleconnections. To address this, explainable artificial intelligence (XAI) offers a promising alternative.
In this study, we test the hypothesis that the observed trend is partially driven by changes in the tropical oceans, which can influence midlatitude weather patterns through tropical-extratropical teleconnections. Using an explainable neural network approach, we aim to identify key tropical regions that drive the midlatitude wave-5 pattern on subseasonal timescales. The methodology is composed of two steps. First, the neural network is trained to predict the wave-5 pattern using tropical outgoing longwave radiation (OLR) fields as input. Next, we apply layer-wise relevance propagation, an explainability technique, to identify which input features are most important for accurate predictions. This process generates heat maps highlighting tropical regions that are important for the generation of a wave-5 pattern. Subsequently, changes in sea surface temperatures (SSTs) and OLR in the identified regions can be assessed as well as their correlation to the trend in the Northern Hemisphere circulation. We will present some preliminary outputs of this analysis.
How to cite: Stoffels, R., Coumou, D., and Galfi, V. M.: Using an explainable neural network to identify tropical drivers of the Northern Hemisphere wave-5 trend pattern, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6885, https://doi.org/10.5194/egusphere-egu25-6885, 2025.
Accurate and reliable future climate information is key for successful implementation of climate change adaptation plans especially on regional scales. Predicting the winter climate over Eurasia is challenging as both the initialized predictions and uninitialized climate projections show limited skill in reproducing observed variability on multi-annual to decadal timescales. It has been long recognized that the climate over Eurasia is strongly influenced by the North Atlantic Oscillation (NAO), especially in winter. The observed NAO indices show strong year to year variations that can be associated with climate conditions in Europe and Asia. Numerous efforts have been made to use NAO as one of the major predictors for European climate. However, the strength and spatial patterns of the NAO-related teleconnections vary with time, for example on multi-annual to decadal timescales, resulting in limited success in predictions on these time scales.
This study presents a novel approach to constrain variability in projection simulations over Eurasia by exploiting the teleconnection between the North Atlantic Oscillation (NAO) and the surface air temperature in the northern hemisphere. The constrained ensemble shows significantly higher skill and added value in predicting the multi-annual winter surface air temperature over Eurasia as compared to both the unconstrained ensemble of historical simulations and the initialized decadal predictions. The sensitivity analysis suggests that the constraining based on teleconnection during the previous 15 to 20 winter seasons is optimum for skillful predictions of multi-annual to decadal mean winter climate over Eurasia.
How to cite: Mahmood, R., Yang, S., and G. Donat, M.: Skillful predictions of Eurasian winter climate by constraining variability in CMIP6 simulations using NAO-temperature teleconnections, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1485, https://doi.org/10.5194/egusphere-egu25-1485, 2025.
Dimensionality reduction techniques are powerful for extracting modes of climate variability from observational data sets and climate model output. Over the past decades, multiple studies have shown that dominant climate patterns can be identified, and that climate evolution can be approximately linearized in such subspaces. In this work, we apply novel dimensionality reduction techniques to global climate data sets. In particular, we optimize such methods for finding patterns that maximize their inherent predictability on multi-annual time scales. We develop a fully Bayesian framework. The record of high-quality climate data sets (20-100 years) is relatively short compared to our predictability time scales of interest (1-10 years). This necessarily causes large uncertainty in data-driven analyses of internal climate variability due to sampling variability and biases. In a Bayesian analysis, we are able to rigorously quantify the uncertainty in observed internal climate variability: both in the spatial patterns, and in their dynamic time evolution.
We use linear inverse modeling to represent the climate dynamics in a subspace that optimizes predictability measures. We then use advanced Bayesian methods to calibrate the parameters of the linear model. The resulting uncertainty analysis allows to identify which climate modes – and interactions between modes – are well- or poorly-constrained within the observational record. This novel method further allows to explore if climate models can reproduce the linearized dynamics within observational uncertainties, or if they fail in representing some specific modes of climate variability.
While still in its early stages, this research is aimed at addressing key climate predictability challenges, in particular identifying the factors that contribute to accurate and reliable multi-annual climate predictions.
How to cite: Verjans, V. and Donat, M.: Bayesian uncertainty quantification of internal climate variability, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3808, https://doi.org/10.5194/egusphere-egu25-3808, 2025.
The summer climate in the Northern Hemisphere during recent decades has shown distinct trend patterns, with warming hotspots that spatially match with the ridges of a circumpolar atmospheric wave pattern. The drivers behind this wave-like trend and warming pattern are not yet well understood. On the one hand, the CMIP6 multi-model ensemble mean presents a high degree of spatial pattern correlation over some regions but at a very small magnitude relative to observations. When considering individual single-model ensembles, however, we find: (i) a substantial spread in the forced response across models and (ii) a large spread in pattern similarity across the different ensemble members of the same models. This suggests that a combination of both forcing and internal climate variability may have contributed to the observed changes in atmospheric circulation. In ongoing work we are aiming to better understand the specific roles of forcing and climate variability, e.g., by investigating specific composites of those simulations most closely resembling the observed trends or by constraining ocean temperature variability patterns.
How to cite: Marcet-Carbonell, G., Donat, M. G., and Delgado-Torres, C.: Understanding the recent changes in summer atmospheric circulation on the Northern Hemisphere: the roles of external forcing and sea surface temperature variability., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3999, https://doi.org/10.5194/egusphere-egu25-3999, 2025.
Ever-worsening climate change increases near-surface air temperatures for almost the entire Earth and threatens living organisms and human society. While annual mean changes are frequently used to quantify past and expected future changes, the increase is actually rarely uniform throughout the year.
The shape of the annual cycle and its changes differ significantly between regions around the globe. Therefore, performing a global analysis implies the necessity to focus on diagnostics that can be evaluated for all these different shapes (e.g., single and double waves, different timing of seasons, etc.). Many previous studies relied on Fourier-transform-based methods, which assume a sinusoidal shape of the mean annual cycle. Here, we introduce an innovative approach based on functional data analysis. The evolution of the mean annual cycle is estimated from daily long-term mean temperature values, which are converted to functional form. This way, we can assess arbitrary shapes of the annual cycle. We concentrate on diagnostics that evaluate the change in absolute temperature, its seasonal slope, and the position of the maximum. We analyze two reanalysis datasets (coupled CERA20C and atmospheric ERA5) and a subset of CMIP6 Earth system models (ESMs). Recent changes in the second half of the 20th century are assessed, and the ability of ESMs to represent them is evaluated. Then, the changes projected for the end of the 21st century under the SSP3-7.0 pathway are analyzed.
Among other results, we highlight distinct differences between the two reanalyses, especially over equatorial and polar regions. Further, the projections show, for example, different rates of warming between seasons, resulting in changes in the amplitude. The largest amplitude increase is projected over the Mediterranean region and the largest decrease over the Arctic Ocean.
How to cite: Holtanová, E., Brunner, L., and Koláček, J.: Quantifying changes in seasonal temperature variations using a functional data analysis approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17493, https://doi.org/10.5194/egusphere-egu25-17493, 2025.
The flood event of April/May 2024 that hit the Southernmost State of Brazil, Rio Grande do Sul, broke local records, with rivers reaching their highest level in recorded history. Around 2.4 million people are estimated to have been affected by the flood, with hundreds of thousands displaced and/or without access to potable water and electricity in their homes. Extreme precipitation, linked to a negative surface pressure anomaly, as suggested in the ERA5 dataset, was the primary driver.
While attributing extreme precipitation events is an established practice, less work has been done to directly attribute river flood events to climate change, often due to a lack of long-term data in the region of interest. This study explores the feasibility of employing the existing attribution framework for attributing extreme discharge events.
We analyzed daily precipitation and river discharge data from over 40 stations (1960–2023, <10% missing) using two approaches. First, a "factual" distribution was developed using all the data available. A “counterfactual” distribution was obtained by fitting a distribution with the global mean surface temperature as covariate and a constant dispersion parameter, and then deriving the distribution assuming a 1.2°C cooler world. Second, the data was divided into two separate periods: 1960-1991 (“past”) and 1992-2023 (“present”). In both cases, differences in extreme values between these distributions were statistically assessed. Additionally, the surface pressure anomaly in ERA5 was used for analog attribution study, to assess the significance of the changes in surface pressure, precipitation, temperature, and discharge fields, between the “past” and “present” time periods.
Hydrological simulations performed with the CETEMPS Hydrological Model (CHyM) coupled with the CORDEX (Coordinated Downscaling Experiment)-CORE models output, both for a historical period and under the rcp85 scenario, were also used. The model validation done for the historical period, comparing CHyM outputs against discharge station data, showed quite good agreement between the two for several statistics. Both the hydrological simulations and the regional climate CORDEX-CORE simulations were used in the analog attribution study to confirm the attribution of the event to global warming. This analysis investigates the potential of integrating hydrological modeling and observational discharge data to advance the attribution of extreme flood events to climate change.
How to cite: Vargas-Heinz, L., Lu, C., and Coppola, E.: Attribution of flood event: a case study of the April/May 2024 floods in Southern Brazil, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5784, https://doi.org/10.5194/egusphere-egu25-5784, 2025.
Earth System Modelling and Observations (ESMO) is a new core project of the World Climate Research Project (WCRP) that coordinates, advances, and facilitates all modelling, data assimilation and observational activities within WCRP, working jointly with all other WCRP projects. Our mission is to facilitate the coordination and advancement of climate modeling and observational efforts. Through collaborative approaches, interdisciplinary partnerships, and identification of critical research gaps ESMO aims to enhance the accuracy, reliability, and accessibility of climate data and projections. Alongside three pre-existing Working Groups - on Coupling Modelling (WGCM), Numerical Experimentation (WGNE), and Sub-seasonal to Interdecadal Prediction (WGSIP), an additional working group on Observations for Researching Climate (WGORC) has now been established. WGORC in particular will focus on observations and needs for observation across WCRP, including observations for reanalyses and emerging technologies. This, alongside the other WGs focused on modelling, will be instrumental in identifying gaps and bridging research communities in climate science. Here, we present the exciting new structure of ESMO, and how we hope to bring together experts across modelling and observational disciplines, to further scientific advances.
How to cite: Niraula, B., Pasqualetto, S., and Adloff, F.: Advancing climate research through the WCRP core project on Earth System Modelling and Observations (ESMO), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20122, https://doi.org/10.5194/egusphere-egu25-20122, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 5
EGU25-12181 | ECS | Posters virtual | VPS5
Seasonal changes in evaporation and potential evapotranspiration under different scenarios of climate change on the territory of UkraineMon, 28 Apr, 14:00–15:45 (CEST) | vP5.7
Evaporation and potential evapotranspiration are components of hydrological cycle that represent the loss of water from the surface and vegetation to the atmosphere. Potential evapotranspiration is a theoretical index that demonstrates the maximum evaporation and transpiration rates assuming sufficient water availability in soil and canopy. Six identical Regional Climate Models (RCMs) of Euro-CORDEX project were selected in order to obtain a unified ensemble for both characteristics for estimation under RCP2.6, RCP4.5 and RCP8.5 scenarios for the middle (2021-2050) and the end of the 21st century (2071-2100) for Ukraine. ERA5 observational dataset is used as a baseline climate normal (1991-2020) for tracking the future changes. In this study we applied a quantile mapping approach for bias correction for smoothing systematic errors between observational and simulated datasets. For the baseline period, the sums of evaporation varied mainly between 20-30 mm in winter to 250-290 mm in summer, with the exception of the Carpathians and southern regions near marine coastal areas (more than 300 mm). Climate normals of evapotranspiration were zonally distributed with the exception of mountainous region and varies from 20-50 mm in winter to 290-550 mm in summer. The most tremendous changes of evaporation are expected to occur in winter. In general, during the following 30-year period of 2021-2050, the most significant increase by 8-18% (compared to 1991-2020 baseline) would be expected for RCP4.5 with more pronounced increase during 2071-2100, reaching its highest values up to 40% under RCP8.5 The maximum rates are observed in the Carpathians and the northeast of Ukraine. In contrast, evapotranspiration in winter is expected to increase only by 1-6% during 2021-2050 for all RCPs and 12-22% by the end of the century. The Carpathians will face even a decrease by -4%. Changes in evaporation will be lower for the spring season, with changes by 2-4% in 2021-2050 and 6-12% by the end of the century. The highest spring changes up to 28% also will occur in the Carpathians. The same rates are estimated for evapotranspiration, for which the sharpest changes are 10-16% under RCP 8.5 for 2081-2100 In comparison to winter and spring, summer and autumn seasons will face much slower changes. Moreover, summer season will be characterized by a decrease in evaporation at a rate up to -2..-4% under RCP2.6 and varying within ±1% for other scenarios by the mid-century, showing the typical tendencies for so called “evaporation paradox”. In 2071-2100, the decrease can reach by up to-6% for RCP4.5 and RCP8.5. It must be noted the different tendency for evapotranspiration with an increase by 1-6% in general for all RCPs in 2021-2050, and maximum up to 14% by the end of the century. For autumn the most typical increase in both parameters is within 2-6% for all RCPs, with the highest rates of evaporation in the Carpathians up to 15%. The obtained results show the importance of considering evaporation in future water management, agriculture and food security in Ukraine, highlighting the seasons and regions with it significant changes.
How to cite: Rybchynska, V., Pysarenko, L., Pushkar, H., Savenets, M., and Osadchyi, V.: Seasonal changes in evaporation and potential evapotranspiration under different scenarios of climate change on the territory of Ukraine, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12181, https://doi.org/10.5194/egusphere-egu25-12181, 2025.
